使用OpenCV實現攝像頭測距 [Find distance from camera to object/marker using Python and OpenCV]  (GOOGLE:OPENCV 測距)

資料來源:
https://zhuanlan.zhihu.com/p/63149294
https://www.pyimagesearch.com/2015/01/19/find-distance-camera-objectmarker-using-python-opencv/

https://github.com/zxdefying/OpenCV_project/tree/master/distance_to_camera

GITHUB:https://github.com/jash-git/Find-distance-from-camera-to-object-using-Python-and-OpenCV

前置動作+原理說明:(使用相似三角形計算物體到相機的距離)

    假設物體的寬度為W，將其放到離相機距離為D 的位置，然後對物體進行拍照。在照片上量出物體的像素寬度P，於是可以得出計算相機焦距F 的公式：

F =（P x D）/ W

    比如我在相機前24 英寸距離（D=24 inches）的位置橫著放了一張8.5 x 11 英寸（W=11 inches）的紙，拍照後通過圖像處理得出照片上紙的像素寬度P=248 pixels。

所以焦距F 等於：

F =（248px x 24in）/ 11in = 543.45

    此時移動相機離物體更近或者更遠，我們可以應用相似三角形得到計算物體到相機的距離的公式：

D’=（寬x F）/ P

PDF

Code:

from imutils import paths
import numpy as np
import imutils
import cv2

# initialize the known distance from the camera to the object, which
# in this case is 24 inches
KNOWN_DISTANCE = 24.0

# initialize the known object width, which in this case, the piece of
# paper is 12 inches wide
KNOWN_WIDTH = 11.0

def get_focalLength():
    # load the furst image that contains an object that is KNOWN TO BE 2 feet
    # from our camera, then find the paper marker in the image, and initialize
    # the focal length
    image = cv2.imread("./2ft.jpg")
    marker = find_marker(image)
    focalLength = (marker[1][0] * KNOWN_DISTANCE) / KNOWN_WIDTH

    return focalLength


def find_marker(image):
    # convert the image to grayscale, blur it, and detect edges
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    gray = cv2.GaussianBlur(gray, (5, 5), 0)
    edged = cv2.Canny(gray, 35, 125)

    # the contour of paper is not closed, so apply close operation(dilate and erode)
    kernel = np.ones((3, 3), np.uint8)
    close = cv2.morphologyEx(edged, cv2.MORPH_CLOSE, kernel)

    # find the contours in the edged image and keep the largest one;
    # we'll assume that this is our piece of paper in the image
    cnts = cv2.findContours(close.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
    cnts = imutils.grab_contours(cnts)
    c = max(cnts, key = cv2.contourArea)

    # compute the bounding box of the of the paper region and return it
    return cv2.minAreaRect(c)


def distance_to_camera(knownWidth, focalLength, perWidth):
    # compute and return the distance from the maker to the camera
    return (knownWidth * focalLength) / perWidth


if __name__ == "__main__":

    focalLength = get_focalLength()

    # loop over the images
    for imagePath in sorted(paths.list_images("images")):
        # load the image, find the marker in the image, then compute the
        # distance to the marker from the camera
        image = cv2.imread(imagePath)
        marker = find_marker(image)
        inches = distance_to_camera(KNOWN_WIDTH, focalLength, marker[1][0])

        # draw a bounding box around the image and display it
        box = cv2.cv.BoxPoints(marker) if imutils.is_cv2() else cv2.boxPoints(marker)
        box = np.int0(box)
        cv2.drawContours(image, [box], -1, (0, 255, 0), 2)
        cv2.putText(image, "%.2fft" % (inches / 12),
            (image.shape[1] - 200, image.shape[0] - 20), cv2.FONT_HERSHEY_SIMPLEX,
            2.0, (0, 255, 0), 3)
        cv2.imshow(imagePath.split('/')[-1], image)

    if cv2.waitKey(0) & 0xFF == ord('q'):
        cv2.destroyAllWindows()